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Record W4392285025 · doi:10.1037/tmb0000125

Smartphones undermine social connectedness more in men than women: A mini mega-analysis.

2024· article· en· W4392285025 on OpenAlexaff
Matthew Leitao, Jason Proulx, Kostadin Kushlev

Bibliographic record

VenueTechnology Mind and Behavior · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMega-Social connectednessSociologyPsychologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Though smartphones have been shown to undermine well-being and social connection, evidence also suggests that these effects depend on when and how people use their phones. To examine whether the effects of phones on well-being (affect valence) and social connection depend on the situation, we compiled data across eight published and unpublished experiments where phone use was manipulated (N = 1,778). These experiments included situations ranging from parents visiting a science museum with their children, eating a meal with a group of strangers, to looking for an unfamiliar building. We found that phones have a significant negative impact on people’s feelings of social connectedness across situations. The impact of phones on well-being, however, depended on the situation: Phones negatively impacted well-being when used during ongoing social interactions, but not when used to find information relevant to current goals. Our large data set also allowed us to examine whether the effects of phones depend on individual differences. We found that gender moderated the effects of phones on social connectedness, whereby phones negatively impacted men more than women. Overall, even after including unpublished studies with nonsignificant findings, we find that the negative effects of phones on well-being and social connection persist. Going beyond past research, however, we also show that these negative effects on social connection are driven by men more so than women.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.340
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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